v1.0 · London · 2026

SimonRendonArango.

I build systems that turn messy data into clarity.

Software and AI engineer. Currently shipping LLM-backed financial tooling at Untap. On the side I build LFM, a live football model, and EvalLens, an open-source LLM eval framework.

Role
Software · AI engineer
Based
London, UK
Get in touchSee work
About

Software and AI Engineer

Hi, I’m Simon Rendon Arango, a Software and AI Engineer passionate about building intelligent systems that turn complex data into actionable insights. I hold an MSc in Computing (Software Engineering) from Imperial College London and a BSc in Systems and Computing Engineering from Universidad de los Andes. My professional journey spans startups and fintech, where I’ve designed AI-driven KPI extraction modules, developed scalable backend services, and built user-facing products at companies like Untap, Glamper, and Nequi (Bancolombia).

I’m also a curious creator, constantly exploring new technologies and side projects at the intersection of AI, data, and design. I thrive in fast-paced, collaborative environments where ambitious ideas meet rigorous execution — and I’m always looking for opportunities to push the boundaries of what’s possible with software and AI.

Selected work

Things I have shipped.

Newest first.
Experience

Where I have worked.

Nov 2024 — Present

Software Engineer

· Untap

Developed and maintained software for private equity investment management using Java, Python, and JavaScript

  • React
  • JavaScript
  • Java
  • Vertex
  • Gemini
  • GCP
Mar 2023 — Sep 2023

Software Engineer

· Glamper

Developed and maintained the Glamper web application using Vue.js and Nuxt.js, enhancing user experience and functionality

  • Vue
  • JavaScript
  • Nuxt
  • Python
  • AWS
Jul 2021 — Jan 2022

Intern Software Engineer

· Bancolombia

Developed internal AWS–Jira connectors to automate incident tracking, improving response time for infrastructure tickets.

  • Python
  • JavaScript
  • AWS
Education
2018 — 2023
Bogotá, Colombia

Bachelor of Science in Systems and Computing Engineering

· Universidad de los Andes

Data Structures · Algorithm design and analysis · Software Engineering · Mobile and Web Development · Software Architecture

  • Built a strong foundation in computer science, software development, and machine learning.
  • Completed hands-on projects including data analysis, algorithm design, and AI applications.
Sep 2023 — Sep 2024
London, United Kingdom

Master of Science in Computing (Software Engineering)

· Imperial College London

Reinforcement Learning · Deep Learning · Software Engineering · Machine Learning · Natural Language Processing

  • Specialized in advanced software engineering principles, machine learning, and AI.
  • Conducted research and projects on scalable software systems and intelligent applications.
Skills

What I reach for.

Core is deepest. Strong is production-ready. Working is honest.
CoreDeepest and most frequently used
14
  • GitDevOps
  • GitHubDevOps
  • JavaScriptFrontend
  • JupyterML · Data
  • LLMsML · Data
  • Next.jsFrontend
  • Node.jsBackend · Cloud
  • NumPyML · Data
  • PandasML · Data
  • PythonBackend · Cloud
  • RAGML · Data
  • ReactFrontend
  • Tailwind CSSFrontend
  • TypeScriptFrontend
Strong
Production-ready, used often
  • AWS
  • CI/CD
  • Data Visualization
  • Deep Learning
  • Design
  • Docker
  • Express
  • Framer Motion
  • GCP
  • GitLab
  • Java
  • MCP
  • Reinforcement Learning
  • Scikit-learn
  • shadcn/ui
  • TensorFlow
Working
Comfortable, actively improving

C++HPCNuxt.jsPyTorchVue

Ask

Ask me anything about my work.

A small RAG agent over my experience. It cites what it knows and admits what it does not. The same agent is always available bottom-right.

Chat with Simon’s agent
RAG over CV
Ask something concrete — experience, stack, projects. I will keep it short.
Try
Product · in progress

Live Football Model.

Open LFM

Real-time match probabilities, an embedding explorer, and a Monte Carlo simulation lab. Streaming ingestion, online inference, vector search — a football engine built to read matches as living systems.

live· 73'
ARS 1  —  CHE 0
xG 1.8  /  1.2
Win · Draw · Loss0.62 · 0.24 · 0.14
live · embeddings · simulationOpen LFM
Open source

EvalLens.

Open EvalLens

A systematic evaluation framework for LLM outputs. Reproducible, versioned metrics across model and prompt variants — catches regressions before they ship instead of after.

run · gpt-4.1 vs gpt-4.0
5 evals · 1 regression
evalprevnowbar
  • extract_kpis0.910.93
  • summarise_doc0.820.84
  • classify_intent0.880.78
  • rank_candidates0.740.79
  • route_support0.670.69
reproducible · CI-ready · regressionsOpen EvalLens
Contact

Work together.

Roles, collaborations, or interesting problems — send a note.